




import unittest

from knapsack import greedy_knapsack as kp




class 						UpperCamelCase__      (unittest.TestCase	):

    '''simple docstring'''



    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : List[Any]     					=   [10, 20, 30, 40, 50, 60]
           lowerCamelCase  : Dict     					=   [2, 4, 6, 8, 10, 12]
           lowerCamelCase  : Optional[Any]     					=   100
           self.assertEqual(kp.calc_profit(lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	)	,    210	)







    def        _lowercase	(       self	)	-> List[Any]:
           self.assertRaisesRegex(lowerCamelCase_	,    "max_weight must greater than zero."	)







    def        _lowercase	(       self	)	-> Union[str, Any]:
           self.assertRaisesRegex(lowerCamelCase_	,    "Weight can not be negative."	)







    def        _lowercase	(       self	)	-> Tuple:
           self.assertRaisesRegex(lowerCamelCase_	,    "Profit can not be negative."	)







    def        _lowercase	(       self	)	-> Dict:
           self.assertRaisesRegex(lowerCamelCase_	,    "max_weight must greater than zero."	)







    def        _lowercase	(       self	)	-> int:
           self.assertRaisesRegex(
               lowerCamelCase_	,    "The length of profit and weight must be same."	)


if __name__ == "__main__":
       unittest.main()







import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
